{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T13:24:43Z","timestamp":1770729883486,"version":"3.49.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,16]]},"DOI":"10.1109\/aibthings58340.2023.10292477","type":"proceedings-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T18:51:40Z","timestamp":1698691900000},"page":"1-6","source":"Crossref","is-referenced-by-count":13,"title":["Computational Complexity Reduction Techniques for Deep Neural Networks: A Survey"],"prefix":"10.1109","author":[{"given":"Md. Bipul","family":"Hossain","sequence":"first","affiliation":[{"name":"University of South Alabama,Electrical and Computer Engineering,Mobile,AL,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Gong","sequence":"additional","affiliation":[{"name":"University of South Alabama,Electrical and Computer Engineering,Mobile,AL,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Shaban","sequence":"additional","affiliation":[{"name":"University of South Alabama,Electrical and Computer Engineering,Mobile,AL,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/computers12030060"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09816-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1212\/01.WNL.0000166914.38327.BB"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.241"},{"key":"ref5","article-title":"Learning both weights and connections for efficient neural network","volume":"28","author":"Han","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref6","article-title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","author":"Zhu","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01152"},{"key":"ref8","article-title":"Dynamic network surgery for efficient dnns","volume":"29","author":"Guo","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref9","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"Frankle","year":"2018"},{"key":"ref10","article-title":"Pruning Deep Neural Networks from a Sparsity Perspective","author":"Diao","year":"2023"},{"key":"ref11","article-title":"Pruning filters for efficient convnets","author":"Li","year":"2016"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11182887"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157329"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03293-x"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref16","article-title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures","author":"Hu","year":"2016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2874634"},{"key":"ref18","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","volume":"28","author":"Courbariaux","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref19","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1","author":"Courbariaux","year":"2016"},{"key":"ref20","article-title":"Loss-aware binarization of deep networks","author":"Hou","year":"2016"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_20"},{"key":"ref22","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"Han","year":"2015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11663"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref25","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"Krishnamoorthi","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107659"},{"key":"ref28","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref29","article-title":"Fitnets: Hints for thin deep nets","author":"Romero","year":"2014"},{"key":"ref30","first-page":"14759","article-title":"Task-oriented feature distillation","volume":"33","author":"Zhang","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11179-7_36"},{"key":"ref33","first-page":"4985","article-title":"StrassenNets: Deep learning with a multiplication budget","volume-title":"International Conference on Machine Learning","author":"Tschannen"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.435"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10162004"},{"key":"ref36","article-title":"Neural networks with few multiplications","author":"Lin","year":"2015"},{"key":"ref37","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref39","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size","author":"Iandola","year":"2016"},{"key":"ref40","first-page":"11711","article-title":"Mcunet: Tiny deep learning on iot devices","volume":"33","author":"Lin","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"ref42","article-title":"Neural architecture search with reinforcement learning","author":"Zoph","year":"2016"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01166"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"}],"event":{"name":"2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)","location":"Mount Pleasant, MI, USA","start":{"date-parts":[[2023,9,16]]},"end":{"date-parts":[[2023,9,17]]}},"container-title":["2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10292429\/10291015\/10292477.pdf?arnumber=10292477","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T10:33:09Z","timestamp":1709375589000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10292477\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,16]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/aibthings58340.2023.10292477","relation":{},"subject":[],"published":{"date-parts":[[2023,9,16]]}}}